Members of the New Scientist Book Club give their take on Sierra Greer's award-winning science-fiction novel Annie Bot, our read for February - and the needle swings wildly from positive to negative Annie Bot by Sierra Greer was the Book Club's January read The New Scientist Book Club moved on from reading a classic piece science fiction in December - Iain M. Banks's - to an award-winning sci-fi novel in January: Sierra Greer's, which won the Arthur C. Clarke prize in 2025. I must admit, I was nervous to announce this one to my fellow readers. is the story of a sex robot, owned by a controlling and abusive man. It gets very dark in places, it has a number of sex scenes, and I wanted to make sure you all knew what you were getting into before getting started. That cupboard scene, some way into the book, was super disturbing, for example. It turns out my wariness was warranted.

For years now, an online shop called Super Yaki has been selling T-shirts and hats printed with the message "Judy Greer should've been the lead." That there is a market for such merch is a testament to just how beloved an actress Greer is, despite her reputation for always playing the sidekick rather than the main character. This month, though, all those T-shirt wearers' wishes have come true, sort of: The 49-year-old receives top billing in a movie that debuted on more than 3,000 screens last week. If you're wondering why you haven't heard of it, here comes the catch: Greer's lead role is in a Christian family movie from the son of the guy who co-wrote the Left Behind books. Greer plays a mother who takes on the challenge of directing her church's annual Christmas play in The Best Christmas Pageant Ever, directed by Dallas Jenkins, creator of Christian miniseries The Chosen, and based on the 1972 children's book of the same name.

In August, a thoughtful blogger, Tanner Greer, posed an interesting question to the Silicon Valley crowd: "What are the contents of the'vague tech canon'? If we say it is 40 books, what are they?" He was using the term "canon" in the sense of "the collection of works considered representative of a period or genre", but astutely qualifying it to stop Harold Bloom – the great literary critic who spent his life campaigning for a canon consisting of the great works of the past (Shakespeare, Proust, Dante, Montaigne et al) – spinning in his grave. Greer's challenge was immediately taken up by Patrick Collison, co-founder with his brother, John, of the fintech giant Stripe (market value 65bn) and thus among the richest Irishmen in history. Unusually among tech titans, Collison is a passionate advocate of reading, and so it was perhaps predictable that he would produce a list of 43 books – adding a caveat that it wasn't "the list of books that I think one ought to read – it's just the list that I think roughly covers the major ideas that are influential here".

This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating higher-level reasoning abilities. Our proposed method employs language-based representations to identify novel scenes, emphasizing the dual purpose of safety takeover responses and active learning. The research presents a clustering experiment using Contrastive Language-Image Pretrained (CLIP) embeddings to organize datasets and detect novelties. We find that the proposed algorithm effectively isolates novel scenes from a collection of subsets derived from two real-world driving datasets, one vehicle-mounted and one infrastructure-mounted. From the generated clusters, we further present methods for generating textual explanations of elements which differentiate scenes classified as novel from other scenes in the data pool, presenting qualitative examples from the clustered results. Our results demonstrate the effectiveness of language-driven embeddings in identifying novel elements and generating explanations of data, and we further discuss potential applications in safe takeovers, data curation, and multi-task active learning.

After decades of creating war machines and home cleaning appliances, iRobot agreed to be acquired by Amazon for $1.7 billion, according to a joint statement by the two companies. If the deal goes through, it would give Amazon access to yet another wellspring of personal data: interior maps of Roomba owners' homes. Those Roombas work in part by using sensors to map the homes they operate in. In a 2017 Reuters interview, iRobot CEO Colin Angle suggested the company might someday share that data with tech companies developing smart home devices and AI assistants. Amazon declined to respond to questions about how it would use that data, but combined with other recent acquisition targets, the company could wind up with a comprehensive look at what's happening inside people's homes.

Music is an indispensable element in film: it establishes atmosphere and mood, drives the viewer's emotional reactions, and significantly influences the audience's interpretation of the story. In a recent paper published in PLOS ONE, a research team at the USC Viterbi School of Engineering, led by Professor Shrikanth Narayanan, sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone. "By better understanding how music affects the viewer's perception of a film, we gain insights into how film creators can reach their audience in a more compelling way," said Narayanan, University Professor and Niki and Max Nikias Chair in Engineering, professor of electrical and computer engineering and computer science and the director of USC Viterbi's Signal Analysis and Interpretation Laboratory (SAIL). The notion that different film genres are more likely to use certain musical elements in their soundtrack is rather intuitive: a lighthearted romance might include rich string passages and lush, lyrical melodies, while a horror film might instead feature unsettling, piercing frequencies and eerily discordant notes.

Study authors include Professor Shrikanth Narayanan, Timothy Greer, Dillon Knox, and Benjamin Ma. (Images Courtesy of Narayanan, Greer, Knox, and Ma) Music is an indispensable element in film: it establishes atmosphere and mood, drives the viewer's emotional reactions, and significantly influences the audience's interpretation of the story. In a recent paper published in PLOS One, a research team at the USC Viterbi School of Engineering, led by Professor Shrikanth Narayanan, sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone. "By better understanding how music affects the viewer's perception of a film, we gain insights into how film creators can reach their audience in a more compelling way," said Narayanan, University Professor and Niki and Max Nikias Chair in Engineering, professor of electrical and computer engineering and computer science and the director of USC Viterbi's Signal Analysis and Interpretation Laboratory (SAIL). The notion that different film genres are more likely to use certain musical elements in their soundtrack is rather intuitive: a lighthearted romance might include rich string passages and lush, lyrical melodies, while a horror film might instead feature unsettling, piercing frequencies and eerily discordant notes.

All the sessions from Transform 2021 are available on-demand now. There is a significant gap between an organization's ambitions for using artificial intelligence (AI) and the reality of how those projects turn out, Intel chief data scientist Dr. Melvin Greer said in a conversation with VentureBeat founder and CEO Matt Marshall at last week's Transf0rm 2021 virtual conference. One of the key areas is emotional intelligence and mindfulness. The pandemic highlighted this gap: The way people had to juggle home and work responsibilities meant their ability to stay focused and mindful could be compromised, Greer said. This could be a problem when AI is used in a cyberattack, like when someone is trying to use a chatbot or some other adversarial machine learning technique against us. "Our ability to get to the heart of what we're trying to achieve can be compromised when we are not in an emotional state and mindful and present," Greer said.

The idea of responsible artificial intelligence (AI) is spreading far and wide across the U.S. Department of Defense and its surrounding ecosystem. There's been the new data strategy, the responsible AI memo and the newly approved JADC2 strategy that has a massive data component. "The DoD is very much accelerating its path," said Thomas Kenney, chief data officer and director of SOF AI for U.S. Special Operations Command, during day two of the virtual AFCEA/GMU Critical Issues in C4I Symposium. "Our chief data officer at the DoD, David Spirk, is doing herculean work to help the entire DoD move forward," he added. "That new data strategy, as we think about data sharing, is absolutely essential because it creates the conditions for success where we can open doors to data we maybe didn't have access to before or maybe data we didn't even know existed," Kenney said.

June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.